NCA-GENL Question 224
Select 3A team developing a generative AI system for automated content creation identifies potential bias in the training dataset. Which actions should the team take to minimize bias in their AI system?
- A
Perform a thorough audit of the dataset to identify underrepresented groups and rebalance the data accordingly.
- B
Use synthetic data augmentation techniques to artificially increase the representation of underrepresented groups in the dataset.
- C
Avoid pre-training on large, publicly available datasets as they may introduce inherent biases.
- D
Leverage explainability tools to understand how the model makes decisions and adjust training accordingly.
- E
Rely solely on increasing the size of the training dataset to statistically reduce bias.
Show answer and explanation
Correct answers: A, B, D
Explanation
Minimizing bias in AI systems involves multiple proactive steps. Auditing and rebalancing the dataset ensures fairness in representation. Synthetic data augmentation can supplement underrepresented groups, and explainability tools help identify and mitigate biases during model training. Solely relying on dataset size or avoiding pre-training on large datasets are ineffective or impractical measures.
- A. Correct.
Auditing the dataset and rebalancing underrepresented groups is a standard approach to address and minimize data bias effectively.
- B. Correct.
Using synthetic data augmentation ensures that underrepresented groups have sufficient representation in the training process, reducing bias.
- C. Incorrect.
Avoiding pre-training on large datasets entirely is impractical and unnecessary. Instead, biases in such datasets can be mitigated through careful preprocessing and downstream training adjustments.
- D. Correct.
Explainability tools help identify potential biases in the model's decision-making process and provide insights for fine-tuning the model, making this a critical step.
- E. Incorrect.
While increasing the dataset size can help improve overall model performance, it does not directly address inherent biases and may even amplify them if the additional data is similarly biased.